Healthcare's regulatory AI conundrum


It was the last question of the night and it hushed the entire room. An entrepreneur expressed his aggravation about the FDA's antiquated regulatory environment for AI-enabled devices to Dr. Joel Stein of Columbia University. Stein a leader in rehabilitative robotic medicine, sympathized with the startup knowing full well that tomorrow's exoskeletons will rely heavily on machine intelligence. Nodding her head in agreement, Kate Merton of JLabs shared the sentiment. Her employer, Johnson & Johnson, is partnered with Google to revolutionize the operating room through embedded deep learning systems.

How Health Care AI Systems Are Changing Care Delivery - NEJM Catalyst


A nurse avatar named "Molly" who regularly talks with patients about their symptoms and medical needs. Voice-recognition software that helps physicians document clinical encounters. A prescription drug-monitoring platform that can detect patients' opioid misuse. Systems that analyze millions of medical images to help physicians diagnose and predict diseases. Robots that extend the reach of surgeons.

Machine learning in medicine: Addressing ethical challenges


This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The authors received no specific funding for this work. Competing interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: EV has received speaking fees from SwissRe, Novartis R&D Academy, and Google Netherlands. IGC served as a consultant for Otsuka Pharmaceuticals advising on the use of digital medicine for its Abilify MyCite product. IGC is supported by the Collaborative Research Program for Biomedical Innovation Law, which is a scientifically independent collaborative research program supported by Novo Nordisk Foundation.

governance model for the application of AI in health care


As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in health care. These concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments. While there has been extensive discussion about the ethics of AI in health care, there has been little dialogue or recommendations as to how to practically address these concerns in health care. In this article, we propose a governance model that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance of AI in health care. Interest in AI has gone through cyclical phases of expectation and disappointment since the late 1950s because of poor-performing algorithms and computing infrastructure.1 However, the emergence of appropriate computing infrastructure, big data, and deep learning algorithms has reinvigorated interest in artificial intelligence (AI) technology and accelerated its adoption in various sectors.2 While recent approaches to AI, such as machine learning, have only been relatively recently applied to health care, the future looks promising because of the likelihood of improved healthcare outcomes.3,4

Digital R&D


Digital technologies can transform how companies approach clinical development by incorporating valuable insights from multiple sources of data, radically improving the patient experience, enhancing clinical trial productivity, and increasing the amount and quality of data collected in trials. But where is the industry in adopting these transformative technologies? We interviewed 43 leaders across the clinical development ecosystem to understand the current level of adoption of digital technologies and how it can be accelerated. We found that the industry has been slow to digitize its clinical development processes, and that digital adoption varies widely. Even the most advanced organizations are simply piloting several technologies in different areas of clinical development, focusing on piecemeal solutions or new tools to support the existing process. Our research and client experience suggest that digital transformation is a complex, resource-intensive, and lengthy undertaking. But the rewards can be significant: Early adopters can benefit from better access to and engagement with patients, deeper insights, and faster cycle times for products in development. Many in our study expressed a desire to be fast followers, but given the complexity of operationalizing a digital strategy, the reality is that undue delay could put organizations at a competitive disadvantage. At the same time, our research also indicates that biopharma companies and contract research organizations (CROs) will need to overcome several challenges to realize the potential of digital in clinical development: immature data infrastructure and analytics, regulatory considerations, and internal organizational and cultural barriers. Biopharma companies should consider building updated data infrastructure and governance, engaging early with regulators to discuss new technologies, and developing a measured approach to evaluating and implementing technologies within their organizations. CROs can enable this change by advancing interoperable digital platforms and vetting promising technology applications. Cross-industry consortia could help advance the industry as a whole by offering a forum to share early successes and supporting the development of standards. The time to act is now.